Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations1686
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory382.1 KiB
Average record size in memory232.1 B

Variable types

Numeric12
Categorical4
Text12
DateTime1

Alerts

type has constant value "audio_features"Constant
acousticness is highly overall correlated with energyHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
time_signature is highly imbalanced (76.6%)Imbalance
instrumentalness has 706 (41.9%) zerosZeros
key has 152 (9.0%) zerosZeros

Reproduction

Analysis started2025-10-23 02:50:54.347070
Analysis finished2025-10-23 02:51:15.518493
Duration21.17 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

energy
Real number (ℝ)

High correlation 

Distinct624
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66721607
Minimum0.00161
Maximum0.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:15.657692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.00161
5-th percentile0.33125
Q10.551
median0.689
Q30.807
95-th percentile0.932
Maximum0.99
Range0.98839
Interquartile range (IQR)0.256

Descriptive statistics

Standard deviation0.18490778
Coefficient of variation (CV)0.27713328
Kurtosis0.31366186
Mean0.66721607
Median Absolute Deviation (MAD)0.125
Skewness-0.6763368
Sum1124.9263
Variance0.034190886
MonotonicityNot monotonic
2025-10-22T19:51:15.842375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58611
 
0.7%
0.87210
 
0.6%
0.6819
 
0.5%
0.7879
 
0.5%
0.869
 
0.5%
0.6688
 
0.5%
0.7158
 
0.5%
0.7818
 
0.5%
0.8128
 
0.5%
0.5078
 
0.5%
Other values (614)1598
94.8%
ValueCountFrequency (%)
0.001611
0.1%
0.003621
0.1%
0.005271
0.1%
0.041
0.1%
0.05161
0.1%
0.05591
0.1%
0.05611
0.1%
0.05821
0.1%
0.08231
0.1%
0.08671
0.1%
ValueCountFrequency (%)
0.991
0.1%
0.9891
0.1%
0.9881
0.1%
0.9861
0.1%
0.9851
0.1%
0.9822
0.1%
0.9782
0.1%
0.9721
0.1%
0.971
0.1%
0.9671
0.1%

tempo
Real number (ℝ)

Distinct1387
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.07094
Minimum49.305
Maximum209.688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:16.032506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum49.305
5-th percentile80.09
Q1100.05875
median120.001
Q3136.8335
95-th percentile171.9705
Maximum209.688
Range160.383
Interquartile range (IQR)36.77475

Descriptive statistics

Standard deviation27.066029
Coefficient of variation (CV)0.22355513
Kurtosis-0.055617474
Mean121.07094
Median Absolute Deviation (MAD)18.922
Skewness0.43640997
Sum204125.6
Variance732.56994
MonotonicityNot monotonic
2025-10-22T19:51:16.377962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.0385
 
0.3%
144.9414
 
0.2%
119.9924
 
0.2%
159.924
 
0.2%
184.1154
 
0.2%
150.0624
 
0.2%
119.9964
 
0.2%
93.24
 
0.2%
130.0074
 
0.2%
101.0614
 
0.2%
Other values (1377)1645
97.6%
ValueCountFrequency (%)
49.3051
0.1%
50.8271
0.1%
58.3031
0.1%
62.8811
0.1%
63.9251
0.1%
65.0431
0.1%
65.2031
0.1%
67.0861
0.1%
67.2381
0.1%
67.5281
0.1%
ValueCountFrequency (%)
209.6881
0.1%
204.0281
0.1%
203.8121
0.1%
203.0061
0.1%
201.0251
0.1%
199.9971
0.1%
199.8921
0.1%
197.981
0.1%
196.4821
0.1%
196.121
0.1%

danceability
Real number (ℝ)

Distinct551
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6503618
Minimum0.136
Maximum0.979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:16.567556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.136
5-th percentile0.366
Q10.54325
median0.6645
Q30.769
95-th percentile0.8865
Maximum0.979
Range0.843
Interquartile range (IQR)0.22575

Descriptive statistics

Standard deviation0.15772149
Coefficient of variation (CV)0.24251345
Kurtosis-0.31028225
Mean0.6503618
Median Absolute Deviation (MAD)0.1105
Skewness-0.36551786
Sum1096.51
Variance0.024876067
MonotonicityNot monotonic
2025-10-22T19:51:16.746109image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67112
 
0.7%
0.74110
 
0.6%
0.84810
 
0.6%
0.8539
 
0.5%
0.7429
 
0.5%
0.7479
 
0.5%
0.7049
 
0.5%
0.7379
 
0.5%
0.6468
 
0.5%
0.5028
 
0.5%
Other values (541)1593
94.5%
ValueCountFrequency (%)
0.1361
0.1%
0.1381
0.1%
0.1841
0.1%
0.1911
0.1%
0.2021
0.1%
0.2091
0.1%
0.211
0.1%
0.2151
0.1%
0.2351
0.1%
0.2491
0.1%
ValueCountFrequency (%)
0.9791
 
0.1%
0.9751
 
0.1%
0.9741
 
0.1%
0.971
 
0.1%
0.9672
0.1%
0.9642
0.1%
0.9632
0.1%
0.9621
 
0.1%
0.9563
0.2%
0.9511
 
0.1%

playlist_genre
Categorical

Distinct28
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
pop
357 
rock
235 
hip-hop
227 
latin
184 
electronic
148 
Other values (23)
535 

Length

Max length10
Median length7
Mean length5.3244365
Min length3

Characters and Unicode

Total characters8977
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowpop
2nd rowpop
3rd rowpop
4th rowpop
5th rowpop

Common Values

ValueCountFrequency (%)
pop357
21.2%
rock235
13.9%
hip-hop227
13.5%
latin184
10.9%
electronic148
8.8%
gaming100
 
5.9%
ambient61
 
3.6%
r&b50
 
3.0%
arabic50
 
3.0%
punk50
 
3.0%
Other values (18)224
13.3%

Length

2025-10-22T19:51:16.919062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pop357
21.2%
rock235
13.9%
hip-hop227
13.5%
latin184
10.9%
electronic148
8.8%
gaming100
 
5.9%
ambient61
 
3.6%
r&b50
 
3.0%
arabic50
 
3.0%
punk50
 
3.0%
Other values (18)224
13.3%

Most occurring characters

ValueCountFrequency (%)
p1262
14.1%
o1061
11.8%
i843
9.4%
c604
 
6.7%
a591
 
6.6%
n590
 
6.6%
r544
 
6.1%
l487
 
5.4%
e479
 
5.3%
h461
 
5.1%
Other values (15)2055
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)8977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p1262
14.1%
o1061
11.8%
i843
9.4%
c604
 
6.7%
a591
 
6.6%
n590
 
6.6%
r544
 
6.1%
l487
 
5.4%
e479
 
5.3%
h461
 
5.1%
Other values (15)2055
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p1262
14.1%
o1061
11.8%
i843
9.4%
c604
 
6.7%
a591
 
6.6%
n590
 
6.6%
r544
 
6.1%
l487
 
5.4%
e479
 
5.3%
h461
 
5.1%
Other values (15)2055
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p1262
14.1%
o1061
11.8%
i843
9.4%
c604
 
6.7%
a591
 
6.6%
n590
 
6.6%
r544
 
6.1%
l487
 
5.4%
e479
 
5.3%
h461
 
5.1%
Other values (15)2055
22.9%

loudness
Real number (ℝ)

High correlation 

Distinct1311
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.7041311
Minimum-43.643
Maximum1.295
Zeros0
Zeros (%)0.0%
Negative1684
Negative (%)99.9%
Memory size13.3 KiB
2025-10-22T19:51:17.085383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-43.643
5-th percentile-12.26875
Q1-7.95025
median-5.9745
Q3-4.68725
95-th percentile-3.17625
Maximum1.295
Range44.938
Interquartile range (IQR)3.263

Descriptive statistics

Standard deviation3.3770684
Coefficient of variation (CV)-0.50372948
Kurtosis21.94657
Mean-6.7041311
Median Absolute Deviation (MAD)1.5205
Skewness-3.236981
Sum-11303.165
Variance11.404591
MonotonicityNot monotonic
2025-10-22T19:51:17.274748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.4936
 
0.4%
-5.2985
 
0.3%
-5.0254
 
0.2%
-5.5054
 
0.2%
-5.0984
 
0.2%
-3.3674
 
0.2%
-4.4094
 
0.2%
-3.3444
 
0.2%
-7.0554
 
0.2%
-5.4854
 
0.2%
Other values (1301)1643
97.4%
ValueCountFrequency (%)
-43.6431
0.1%
-37.8221
0.1%
-37.2641
0.1%
-33.7661
0.1%
-27.3381
0.1%
-25.3981
0.1%
-25.2031
0.1%
-24.951
0.1%
-23.0231
0.1%
-21.1341
0.1%
ValueCountFrequency (%)
1.2951
0.1%
0.2541
0.1%
-0.931
0.1%
-0.9591
0.1%
-1.4581
0.1%
-1.541
0.1%
-1.6241
0.1%
-1.7021
0.1%
-1.8261
0.1%
-2.0421
0.1%

liveness
Real number (ℝ)

Distinct628
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17157859
Minimum0.021
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:17.456613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.021
5-th percentile0.061225
Q10.0934
median0.121
Q30.21
95-th percentile0.403
Maximum0.95
Range0.929
Interquartile range (IQR)0.1166

Descriptive statistics

Standard deviation0.12395347
Coefficient of variation (CV)0.72242969
Kurtosis5.8439204
Mean0.17157859
Median Absolute Deviation (MAD)0.04
Skewness2.1038811
Sum289.2815
Variance0.015364462
MonotonicityNot monotonic
2025-10-22T19:51:17.635779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10825
 
1.5%
0.11524
 
1.4%
0.11922
 
1.3%
0.10421
 
1.2%
0.10920
 
1.2%
0.1118
 
1.1%
0.11218
 
1.1%
0.10618
 
1.1%
0.11117
 
1.0%
0.12715
 
0.9%
Other values (618)1488
88.3%
ValueCountFrequency (%)
0.0211
 
0.1%
0.02191
 
0.1%
0.02451
 
0.1%
0.02931
 
0.1%
0.03181
 
0.1%
0.03271
 
0.1%
0.0364
0.2%
0.03651
 
0.1%
0.03671
 
0.1%
0.03681
 
0.1%
ValueCountFrequency (%)
0.951
0.1%
0.9491
0.1%
0.8391
0.1%
0.8321
0.1%
0.8311
0.1%
0.7921
0.1%
0.791
0.1%
0.781
0.1%
0.7241
0.1%
0.6941
0.1%

valence
Real number (ℝ)

Distinct717
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52573654
Minimum0.0348
Maximum0.978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:17.817661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0348
5-th percentile0.145
Q10.339
median0.528
Q30.72
95-th percentile0.912
Maximum0.978
Range0.9432
Interquartile range (IQR)0.381

Descriptive statistics

Standard deviation0.23611322
Coefficient of variation (CV)0.4491094
Kurtosis-0.96787126
Mean0.52573654
Median Absolute Deviation (MAD)0.191
Skewness-0.015955471
Sum886.3918
Variance0.055749452
MonotonicityNot monotonic
2025-10-22T19:51:18.009126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4658
 
0.5%
0.728
 
0.5%
0.577
 
0.4%
0.5357
 
0.4%
0.8867
 
0.4%
0.8387
 
0.4%
0.7747
 
0.4%
0.4377
 
0.4%
0.5647
 
0.4%
0.4467
 
0.4%
Other values (707)1614
95.7%
ValueCountFrequency (%)
0.03481
0.1%
0.03562
0.1%
0.0361
0.1%
0.0382
0.1%
0.03812
0.1%
0.03951
0.1%
0.04541
0.1%
0.05092
0.1%
0.0651
0.1%
0.06821
0.1%
ValueCountFrequency (%)
0.9781
 
0.1%
0.9731
 
0.1%
0.9722
0.1%
0.9712
0.1%
0.972
0.1%
0.9692
0.1%
0.9671
 
0.1%
0.9652
0.1%
0.9644
0.2%
0.9631
 
0.1%
Distinct1033
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:18.384143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length76
Median length59
Mean length15.588375
Min length1

Characters and Unicode

Total characters26282
Distinct characters105
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique719 ?
Unique (%)42.6%

Sample

1st rowLady Gaga, Bruno Mars
2nd rowBillie Eilish
3rd rowGracie Abrams
4th rowSabrina Carpenter
5th rowROSÉ, Bruno Mars
ValueCountFrequency (%)
the100
 
2.3%
lil68
 
1.5%
bad61
 
1.4%
bunny60
 
1.4%
baby31
 
0.7%
mc23
 
0.5%
scott23
 
0.5%
g23
 
0.5%
22
 
0.5%
future22
 
0.5%
Other values (1677)4003
90.2%
2025-10-22T19:51:18.987253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2750
 
10.5%
e2080
 
7.9%
a2044
 
7.8%
i1475
 
5.6%
n1467
 
5.6%
o1315
 
5.0%
r1255
 
4.8%
l1117
 
4.3%
,873
 
3.3%
s773
 
2.9%
Other values (95)11133
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)26282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2750
 
10.5%
e2080
 
7.9%
a2044
 
7.8%
i1475
 
5.6%
n1467
 
5.6%
o1315
 
5.0%
r1255
 
4.8%
l1117
 
4.3%
,873
 
3.3%
s773
 
2.9%
Other values (95)11133
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2750
 
10.5%
e2080
 
7.9%
a2044
 
7.8%
i1475
 
5.6%
n1467
 
5.6%
o1315
 
5.0%
r1255
 
4.8%
l1117
 
4.3%
,873
 
3.3%
s773
 
2.9%
Other values (95)11133
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2750
 
10.5%
e2080
 
7.9%
a2044
 
7.8%
i1475
 
5.6%
n1467
 
5.6%
o1315
 
5.0%
r1255
 
4.8%
l1117
 
4.3%
,873
 
3.3%
s773
 
2.9%
Other values (95)11133
42.4%

time_signature
Categorical

Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
4
1558 
3
 
92
5
 
29
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1686
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
41558
92.4%
392
 
5.5%
529
 
1.7%
17
 
0.4%

Length

2025-10-22T19:51:19.157680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-22T19:51:19.323525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
41558
92.4%
392
 
5.5%
529
 
1.7%
17
 
0.4%

Most occurring characters

ValueCountFrequency (%)
41558
92.4%
392
 
5.5%
529
 
1.7%
17
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41558
92.4%
392
 
5.5%
529
 
1.7%
17
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41558
92.4%
392
 
5.5%
529
 
1.7%
17
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41558
92.4%
392
 
5.5%
529
 
1.7%
17
 
0.4%

speechiness
Real number (ℝ)

Distinct711
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10092633
Minimum0.0232
Maximum0.848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:19.496103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.0284
Q10.0379
median0.0581
Q30.118
95-th percentile0.329
Maximum0.848
Range0.8248
Interquartile range (IQR)0.0801

Descriptive statistics

Standard deviation0.099748327
Coefficient of variation (CV)0.98832804
Kurtosis5.0736638
Mean0.10092633
Median Absolute Deviation (MAD)0.0245
Skewness2.1098835
Sum170.1618
Variance0.0099497287
MonotonicityNot monotonic
2025-10-22T19:51:19.810589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13811
 
0.7%
0.034310
 
0.6%
0.20410
 
0.6%
0.04319
 
0.5%
0.07769
 
0.5%
0.04129
 
0.5%
0.03178
 
0.5%
0.03238
 
0.5%
0.1068
 
0.5%
0.03368
 
0.5%
Other values (701)1596
94.7%
ValueCountFrequency (%)
0.02321
 
0.1%
0.02391
 
0.1%
0.0241
 
0.1%
0.02431
 
0.1%
0.0251
 
0.1%
0.02522
0.1%
0.02532
0.1%
0.02543
0.2%
0.02551
 
0.1%
0.02561
 
0.1%
ValueCountFrequency (%)
0.8481
0.1%
0.6151
0.1%
0.5911
0.1%
0.5741
0.1%
0.532
0.1%
0.5121
0.1%
0.4991
0.1%
0.4882
0.1%
0.4841
0.1%
0.4721
0.1%

track_popularity
Real number (ℝ)

Distinct29
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.80605
Minimum68
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:19.971370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile68
Q171
median75
Q379
95-th percentile88
Maximum100
Range32
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0325319
Coefficient of variation (CV)0.079578502
Kurtosis0.51054111
Mean75.80605
Median Absolute Deviation (MAD)4
Skewness0.89764523
Sum127809
Variance36.391441
MonotonicityNot monotonic
2025-10-22T19:51:20.131332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
70131
 
7.8%
72129
 
7.7%
73126
 
7.5%
68124
 
7.4%
71116
 
6.9%
69104
 
6.2%
74103
 
6.1%
77102
 
6.0%
75100
 
5.9%
7687
 
5.2%
Other values (19)564
33.5%
ValueCountFrequency (%)
68124
7.4%
69104
6.2%
70131
7.8%
71116
6.9%
72129
7.7%
73126
7.5%
74103
6.1%
75100
5.9%
7687
5.2%
77102
6.0%
ValueCountFrequency (%)
1003
 
0.2%
982
 
0.1%
973
 
0.2%
943
 
0.2%
9314
0.8%
926
 
0.4%
909
 
0.5%
8920
1.2%
8828
1.7%
8718
1.1%
Distinct1437
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:20.472015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length56
Median length56
Mean length56
Min length56

Characters and Unicode

Total characters94416
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1224 ?
Unique (%)72.6%

Sample

1st rowhttps://api.spotify.com/v1/tracks/2plbrEY59IikOBgBGLjaoe
2nd rowhttps://api.spotify.com/v1/tracks/6dOtVTDdiauQNBQEDOtlAB
3rd rowhttps://api.spotify.com/v1/tracks/7ne4VBA60CxGM75vw0EYad
4th rowhttps://api.spotify.com/v1/tracks/1d7Ptw3qYcfpdLNL5REhtJ
5th rowhttps://api.spotify.com/v1/tracks/5vNRhkKd0yEAg8suGBpjeY
ValueCountFrequency (%)
https://api.spotify.com/v1/tracks/6ai3ezq4o3huop6dhudph34
 
0.2%
https://api.spotify.com/v1/tracks/6watfblvb0x077xweovc2k4
 
0.2%
https://api.spotify.com/v1/tracks/1es7auahqvapicoh3qmkdl4
 
0.2%
https://api.spotify.com/v1/tracks/13vxuhw3o8yt7vwridqso44
 
0.2%
https://api.spotify.com/v1/tracks/28drn6tqo95mrvo0jqeo5c4
 
0.2%
https://api.spotify.com/v1/tracks/2nyeymeqydifsyytl2bwd64
 
0.2%
https://api.spotify.com/v1/tracks/3tfed7ysjgnifxeleqwx3r3
 
0.2%
https://api.spotify.com/v1/tracks/3mg9jync1nihbthlglzrwp3
 
0.2%
https://api.spotify.com/v1/tracks/1cobocuwyi2ostofolmrs63
 
0.2%
https://api.spotify.com/v1/tracks/4orozzuy6gown4ugqvazmf3
 
0.2%
Other values (1427)1650
97.9%
2025-10-22T19:51:20.950530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/8430
 
8.9%
t7310
 
7.7%
s5640
 
6.0%
p5637
 
6.0%
o4007
 
4.2%
a3972
 
4.2%
c3946
 
4.2%
i3914
 
4.1%
.3372
 
3.6%
12478
 
2.6%
Other values (55)45710
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)94416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/8430
 
8.9%
t7310
 
7.7%
s5640
 
6.0%
p5637
 
6.0%
o4007
 
4.2%
a3972
 
4.2%
c3946
 
4.2%
i3914
 
4.1%
.3372
 
3.6%
12478
 
2.6%
Other values (55)45710
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)94416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/8430
 
8.9%
t7310
 
7.7%
s5640
 
6.0%
p5637
 
6.0%
o4007
 
4.2%
a3972
 
4.2%
c3946
 
4.2%
i3914
 
4.1%
.3372
 
3.6%
12478
 
2.6%
Other values (55)45710
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)94416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/8430
 
8.9%
t7310
 
7.7%
s5640
 
6.0%
p5637
 
6.0%
o4007
 
4.2%
a3972
 
4.2%
c3946
 
4.2%
i3914
 
4.1%
.3372
 
3.6%
12478
 
2.6%
Other values (55)45710
48.4%

uri
Text

Distinct1437
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:21.275539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters60696
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1224 ?
Unique (%)72.6%

Sample

1st rowspotify:track:2plbrEY59IikOBgBGLjaoe
2nd rowspotify:track:6dOtVTDdiauQNBQEDOtlAB
3rd rowspotify:track:7ne4VBA60CxGM75vw0EYad
4th rowspotify:track:1d7Ptw3qYcfpdLNL5REhtJ
5th rowspotify:track:5vNRhkKd0yEAg8suGBpjeY
ValueCountFrequency (%)
spotify:track:6ai3ezq4o3huop6dhudph34
 
0.2%
spotify:track:6watfblvb0x077xweovc2k4
 
0.2%
spotify:track:1es7auahqvapicoh3qmkdl4
 
0.2%
spotify:track:13vxuhw3o8yt7vwridqso44
 
0.2%
spotify:track:28drn6tqo95mrvo0jqeo5c4
 
0.2%
spotify:track:2nyeymeqydifsyytl2bwd64
 
0.2%
spotify:track:3tfed7ysjgnifxeleqwx3r3
 
0.2%
spotify:track:3mg9jync1nihbthlglzrwp3
 
0.2%
spotify:track:1cobocuwyi2ostofolmrs63
 
0.2%
spotify:track:4orozzuy6gown4ugqvazmf3
 
0.2%
Other values (1427)1650
97.9%
2025-10-22T19:51:21.733870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t3938
 
6.5%
:3372
 
5.6%
o2321
 
3.8%
a2286
 
3.8%
r2274
 
3.7%
s2268
 
3.7%
y2267
 
3.7%
p2265
 
3.7%
k2264
 
3.7%
c2260
 
3.7%
Other values (53)35181
58.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)60696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t3938
 
6.5%
:3372
 
5.6%
o2321
 
3.8%
a2286
 
3.8%
r2274
 
3.7%
s2268
 
3.7%
y2267
 
3.7%
p2265
 
3.7%
k2264
 
3.7%
c2260
 
3.7%
Other values (53)35181
58.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t3938
 
6.5%
:3372
 
5.6%
o2321
 
3.8%
a2286
 
3.8%
r2274
 
3.7%
s2268
 
3.7%
y2267
 
3.7%
p2265
 
3.7%
k2264
 
3.7%
c2260
 
3.7%
Other values (53)35181
58.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t3938
 
6.5%
:3372
 
5.6%
o2321
 
3.8%
a2286
 
3.8%
r2274
 
3.7%
s2268
 
3.7%
y2267
 
3.7%
p2265
 
3.7%
k2264
 
3.7%
c2260
 
3.7%
Other values (53)35181
58.0%
Distinct1212
Distinct (%)71.9%
Missing1
Missing (%)0.1%
Memory size13.3 KiB
2025-10-22T19:51:22.187663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length83
Median length60
Mean length17.059941
Min length1

Characters and Unicode

Total characters28746
Distinct characters143
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique922 ?
Unique (%)54.7%

Sample

1st rowDie With A Smile
2nd rowHIT ME HARD AND SOFT
3rd rowThe Secret of Us (Deluxe)
4th rowShort n' Sweet
5th rowAPT.
ValueCountFrequency (%)
the234
 
4.6%
edition88
 
1.7%
deluxe83
 
1.6%
of67
 
1.3%
65
 
1.3%
you51
 
1.0%
a48
 
1.0%
me47
 
0.9%
i41
 
0.8%
love41
 
0.8%
Other values (1882)4282
84.8%
2025-10-22T19:51:22.854393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3362
 
11.7%
e2515
 
8.7%
o1591
 
5.5%
a1522
 
5.3%
i1405
 
4.9%
n1329
 
4.6%
r1167
 
4.1%
t1139
 
4.0%
l973
 
3.4%
s870
 
3.0%
Other values (133)12873
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)28746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3362
 
11.7%
e2515
 
8.7%
o1591
 
5.5%
a1522
 
5.3%
i1405
 
4.9%
n1329
 
4.6%
r1167
 
4.1%
t1139
 
4.0%
l973
 
3.4%
s870
 
3.0%
Other values (133)12873
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3362
 
11.7%
e2515
 
8.7%
o1591
 
5.5%
a1522
 
5.3%
i1405
 
4.9%
n1329
 
4.6%
r1167
 
4.1%
t1139
 
4.0%
l973
 
3.4%
s870
 
3.0%
Other values (133)12873
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3362
 
11.7%
e2515
 
8.7%
o1591
 
5.5%
a1522
 
5.3%
i1405
 
4.9%
n1329
 
4.6%
r1167
 
4.1%
t1139
 
4.0%
l973
 
3.4%
s870
 
3.0%
Other values (133)12873
44.8%
Distinct73
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:23.216133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19
Mean length13.289442
Min length4

Characters and Unicode

Total characters22406
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.5%

Sample

1st rowToday's Top Hits
2nd rowToday's Top Hits
3rd rowToday's Top Hits
4th rowToday's Top Hits
5th rowToday's Top Hits
ValueCountFrequency (%)
hits375
 
9.9%
rock235
 
6.2%
top197
 
5.2%
pop167
 
4.4%
party161
 
4.2%
throwback150
 
3.9%
classics125
 
3.3%
reggaeton101
 
2.7%
gaming100
 
2.6%
tracks100
 
2.6%
Other values (95)2093
55.0%
2025-10-22T19:51:23.905561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2118
 
9.5%
a1804
 
8.1%
s1594
 
7.1%
o1585
 
7.1%
i1367
 
6.1%
t1145
 
5.1%
e1106
 
4.9%
r917
 
4.1%
c843
 
3.8%
n769
 
3.4%
Other values (45)9158
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)22406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2118
 
9.5%
a1804
 
8.1%
s1594
 
7.1%
o1585
 
7.1%
i1367
 
6.1%
t1145
 
5.1%
e1106
 
4.9%
r917
 
4.1%
c843
 
3.8%
n769
 
3.4%
Other values (45)9158
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2118
 
9.5%
a1804
 
8.1%
s1594
 
7.1%
o1585
 
7.1%
i1367
 
6.1%
t1145
 
5.1%
e1106
 
4.9%
r917
 
4.1%
c843
 
3.8%
n769
 
3.4%
Other values (45)9158
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2118
 
9.5%
a1804
 
8.1%
s1594
 
7.1%
o1585
 
7.1%
i1367
 
6.1%
t1145
 
5.1%
e1106
 
4.9%
r917
 
4.1%
c843
 
3.8%
n769
 
3.4%
Other values (45)9158
40.9%
Distinct1437
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:24.335731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters107904
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1224 ?
Unique (%)72.6%

Sample

1st rowhttps://api.spotify.com/v1/audio-analysis/2plbrEY59IikOBgBGLjaoe
2nd rowhttps://api.spotify.com/v1/audio-analysis/6dOtVTDdiauQNBQEDOtlAB
3rd rowhttps://api.spotify.com/v1/audio-analysis/7ne4VBA60CxGM75vw0EYad
4th rowhttps://api.spotify.com/v1/audio-analysis/1d7Ptw3qYcfpdLNL5REhtJ
5th rowhttps://api.spotify.com/v1/audio-analysis/5vNRhkKd0yEAg8suGBpjeY
ValueCountFrequency (%)
https://api.spotify.com/v1/audio-analysis/6ai3ezq4o3huop6dhudph34
 
0.2%
https://api.spotify.com/v1/audio-analysis/6watfblvb0x077xweovc2k4
 
0.2%
https://api.spotify.com/v1/audio-analysis/1es7auahqvapicoh3qmkdl4
 
0.2%
https://api.spotify.com/v1/audio-analysis/13vxuhw3o8yt7vwridqso44
 
0.2%
https://api.spotify.com/v1/audio-analysis/28drn6tqo95mrvo0jqeo5c4
 
0.2%
https://api.spotify.com/v1/audio-analysis/2nyeymeqydifsyytl2bwd64
 
0.2%
https://api.spotify.com/v1/audio-analysis/3tfed7ysjgnifxeleqwx3r3
 
0.2%
https://api.spotify.com/v1/audio-analysis/3mg9jync1nihbthlglzrwp3
 
0.2%
https://api.spotify.com/v1/audio-analysis/1cobocuwyi2ostofolmrs63
 
0.2%
https://api.spotify.com/v1/audio-analysis/4orozzuy6gown4ugqvazmf3
 
0.2%
Other values (1427)1650
97.9%
2025-10-22T19:51:24.843890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/8430
 
7.8%
a7344
 
6.8%
s7326
 
6.8%
i7286
 
6.8%
o5693
 
5.3%
p5637
 
5.2%
t5624
 
5.2%
y3953
 
3.7%
.3372
 
3.1%
12478
 
2.3%
Other values (56)50761
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)107904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/8430
 
7.8%
a7344
 
6.8%
s7326
 
6.8%
i7286
 
6.8%
o5693
 
5.3%
p5637
 
5.2%
t5624
 
5.2%
y3953
 
3.7%
.3372
 
3.1%
12478
 
2.3%
Other values (56)50761
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)107904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/8430
 
7.8%
a7344
 
6.8%
s7326
 
6.8%
i7286
 
6.8%
o5693
 
5.3%
p5637
 
5.2%
t5624
 
5.2%
y3953
 
3.7%
.3372
 
3.1%
12478
 
2.3%
Other values (56)50761
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)107904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/8430
 
7.8%
a7344
 
6.8%
s7326
 
6.8%
i7286
 
6.8%
o5693
 
5.3%
p5637
 
5.2%
t5624
 
5.2%
y3953
 
3.7%
.3372
 
3.1%
12478
 
2.3%
Other values (56)50761
47.0%
Distinct1437
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:25.154981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters37092
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1224 ?
Unique (%)72.6%

Sample

1st row2plbrEY59IikOBgBGLjaoe
2nd row6dOtVTDdiauQNBQEDOtlAB
3rd row7ne4VBA60CxGM75vw0EYad
4th row1d7Ptw3qYcfpdLNL5REhtJ
5th row5vNRhkKd0yEAg8suGBpjeY
ValueCountFrequency (%)
6ai3ezq4o3huop6dhudph34
 
0.2%
6watfblvb0x077xweovc2k4
 
0.2%
1es7auahqvapicoh3qmkdl4
 
0.2%
13vxuhw3o8yt7vwridqso44
 
0.2%
28drn6tqo95mrvo0jqeo5c4
 
0.2%
2nyeymeqydifsyytl2bwd64
 
0.2%
3tfed7ysjgnifxeleqwx3r3
 
0.2%
3mg9jync1nihbthlglzrwp3
 
0.2%
1cobocuwyi2ostofolmrs63
 
0.2%
4orozzuy6gown4ugqvazmf3
 
0.2%
Other values (1427)1650
97.9%
2025-10-22T19:51:25.636423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%
Distinct1408
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:26.085863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length83
Median length56
Mean length16.630486
Min length2

Characters and Unicode

Total characters28039
Distinct characters143
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1184 ?
Unique (%)70.2%

Sample

1st rowDie With A Smile
2nd rowBIRDS OF A FEATHER
3rd rowThat’s So True
4th rowTaste
5th rowAPT.
ValueCountFrequency (%)
199
 
3.7%
feat167
 
3.1%
the133
 
2.5%
you95
 
1.8%
me93
 
1.7%
love73
 
1.3%
i66
 
1.2%
a59
 
1.1%
of52
 
1.0%
with50
 
0.9%
Other values (2008)4436
81.8%
2025-10-22T19:51:26.754194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3737
 
13.3%
e2428
 
8.7%
a1688
 
6.0%
o1589
 
5.7%
i1273
 
4.5%
t1230
 
4.4%
n1163
 
4.1%
r1048
 
3.7%
s851
 
3.0%
l803
 
2.9%
Other values (133)12229
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)28039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3737
 
13.3%
e2428
 
8.7%
a1688
 
6.0%
o1589
 
5.7%
i1273
 
4.5%
t1230
 
4.4%
n1163
 
4.1%
r1048
 
3.7%
s851
 
3.0%
l803
 
2.9%
Other values (133)12229
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3737
 
13.3%
e2428
 
8.7%
a1688
 
6.0%
o1589
 
5.7%
i1273
 
4.5%
t1230
 
4.4%
n1163
 
4.1%
r1048
 
3.7%
s851
 
3.0%
l803
 
2.9%
Other values (133)12229
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3737
 
13.3%
e2428
 
8.7%
a1688
 
6.0%
o1589
 
5.7%
i1273
 
4.5%
t1230
 
4.4%
n1163
 
4.1%
r1048
 
3.7%
s851
 
3.0%
l803
 
2.9%
Other values (133)12229
43.6%
Distinct843
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
Minimum1954-01-01 00:00:00
Maximum2024-11-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-22T19:51:26.952817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:27.164872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

instrumentalness
Real number (ℝ)

Zeros 

Distinct743
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041520426
Minimum0
Maximum0.971
Zeros706
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:27.551250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.025 × 10-6
Q30.00081375
95-th percentile0.3015
Maximum0.971
Range0.971
Interquartile range (IQR)0.00081375

Descriptive statistics

Standard deviation0.15655566
Coefficient of variation (CV)3.7705697
Kurtosis19.571328
Mean0.041520426
Median Absolute Deviation (MAD)6.025 × 10-6
Skewness4.4594857
Sum70.003439
Variance0.024509675
MonotonicityNot monotonic
2025-10-22T19:51:27.746468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0706
41.9%
0.005385
 
0.3%
1.9 × 10-55
 
0.3%
2.56 × 10-64
 
0.2%
0.0003074
 
0.2%
7.97 × 10-64
 
0.2%
0.894
 
0.2%
7.75 × 10-54
 
0.2%
0.0008094
 
0.2%
6.54 × 10-54
 
0.2%
Other values (733)942
55.9%
ValueCountFrequency (%)
0706
41.9%
1.02 × 10-61
 
0.1%
1.04 × 10-61
 
0.1%
1.06 × 10-61
 
0.1%
1.07 × 10-63
 
0.2%
1.11 × 10-61
 
0.1%
1.16 × 10-62
 
0.1%
1.18 × 10-61
 
0.1%
1.19 × 10-61
 
0.1%
1.2 × 10-61
 
0.1%
ValueCountFrequency (%)
0.9711
0.1%
0.9681
0.1%
0.9611
0.1%
0.9531
0.1%
0.9491
0.1%
0.9351
0.1%
0.9281
0.1%
0.9251
0.1%
0.9241
0.1%
0.9221
0.1%
Distinct1223
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:28.110149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters37092
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique932 ?
Unique (%)55.3%

Sample

1st row10FLjwfpbxLmW8c25Xyc2N
2nd row7aJuG4TFXa2hmE4z1yxc3n
3rd row0hBRqPYPXhr1RkTDG3n4Mk
4th row4B4Elma4nNDUyl6D5PvQkj
5th row2IYQwwgxgOIn7t3iF6ufFD
ValueCountFrequency (%)
3rqqmkqevncy4prgke6oc516
 
0.9%
48zismeixniwlzoqghbpqs9
 
0.5%
5ljqux7orbla1qzyibgti18
 
0.5%
0u28p0qvb1qrxpqp5iholh7
 
0.4%
4g1zrsobmefqf6nelkgibi7
 
0.4%
7txgsndsqvmorl6rq9xyzp6
 
0.4%
4iqbfidgotzxedtt9owjqn6
 
0.4%
7ajug4tfxa2hme4z1yxc3n5
 
0.3%
0ax0uxrhg2ceyixtqcqjda5
 
0.3%
1rm6mgv6bcl6nrag8pgozk5
 
0.3%
Other values (1213)1612
95.6%
2025-10-22T19:51:28.597598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5849
 
2.3%
4842
 
2.3%
2835
 
2.3%
1796
 
2.1%
3770
 
2.1%
0764
 
2.1%
6762
 
2.1%
7702
 
1.9%
R649
 
1.7%
d639
 
1.7%
Other values (52)29484
79.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5849
 
2.3%
4842
 
2.3%
2835
 
2.3%
1796
 
2.1%
3770
 
2.1%
0764
 
2.1%
6762
 
2.1%
7702
 
1.9%
R649
 
1.7%
d639
 
1.7%
Other values (52)29484
79.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5849
 
2.3%
4842
 
2.3%
2835
 
2.3%
1796
 
2.1%
3770
 
2.1%
0764
 
2.1%
6762
 
2.1%
7702
 
1.9%
R649
 
1.7%
d639
 
1.7%
Other values (52)29484
79.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5849
 
2.3%
4842
 
2.3%
2835
 
2.3%
1796
 
2.1%
3770
 
2.1%
0764
 
2.1%
6762
 
2.1%
7702
 
1.9%
R649
 
1.7%
d639
 
1.7%
Other values (52)29484
79.5%

mode
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
1
975 
0
711 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1686
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1975
57.8%
0711
42.2%

Length

2025-10-22T19:51:28.784510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-22T19:51:28.931100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1975
57.8%
0711
42.2%

Most occurring characters

ValueCountFrequency (%)
1975
57.8%
0711
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1975
57.8%
0711
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1975
57.8%
0711
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1975
57.8%
0711
42.2%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3380783
Minimum0
Maximum11
Zeros152
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:29.082853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6082082
Coefficient of variation (CV)0.67593767
Kurtosis-1.296985
Mean5.3380783
Median Absolute Deviation (MAD)3
Skewness0.048864907
Sum9000
Variance13.019166
MonotonicityNot monotonic
2025-10-22T19:51:29.245698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1227
13.5%
11172
10.2%
2154
9.1%
5153
9.1%
0152
9.0%
7151
9.0%
9136
8.1%
6134
7.9%
4123
7.3%
8119
7.1%
Other values (2)165
9.8%
ValueCountFrequency (%)
0152
9.0%
1227
13.5%
2154
9.1%
353
 
3.1%
4123
7.3%
5153
9.1%
6134
7.9%
7151
9.0%
8119
7.1%
9136
8.1%
ValueCountFrequency (%)
11172
10.2%
10112
6.6%
9136
8.1%
8119
7.1%
7151
9.0%
6134
7.9%
5153
9.1%
4123
7.3%
353
 
3.1%
2154
9.1%

duration_ms
Real number (ℝ)

Distinct1391
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214562.13
Minimum61673
Maximum547107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:29.425061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum61673
5-th percentile132855.5
Q1176607.75
median211180
Q3244993.25
95-th percentile315120
Maximum547107
Range485434
Interquartile range (IQR)68385.5

Descriptive statistics

Standard deviation58310.748
Coefficient of variation (CV)0.27176627
Kurtosis3.116413
Mean214562.13
Median Absolute Deviation (MAD)34223
Skewness0.99032843
Sum3.6175174 × 108
Variance3.4001433 × 109
MonotonicityNot monotonic
2025-10-22T19:51:29.623986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2560006
 
0.4%
2514245
 
0.3%
2196284
 
0.2%
2593544
 
0.2%
1803044
 
0.2%
2106894
 
0.2%
1958244
 
0.2%
2741924
 
0.2%
1828574
 
0.2%
2282674
 
0.2%
Other values (1381)1643
97.4%
ValueCountFrequency (%)
616732
0.1%
647881
0.1%
695651
0.1%
743791
0.1%
756921
0.1%
784131
0.1%
825091
0.1%
863201
0.1%
867101
0.1%
869841
0.1%
ValueCountFrequency (%)
5471071
 
0.1%
5168931
 
0.1%
5153873
0.2%
4934001
 
0.1%
4568671
 
0.1%
4474402
0.1%
4381201
 
0.1%
4347201
 
0.1%
4295331
 
0.1%
4147201
 
0.1%

acousticness
Real number (ℝ)

High correlation 

Distinct991
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22121953
Minimum1.33 × 10-5
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:29.824636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.33 × 10-5
5-th percentile0.00111
Q10.02305
median0.124
Q30.33475
95-th percentile0.77625
Maximum0.995
Range0.9949867
Interquartile range (IQR)0.3117

Descriptive statistics

Standard deviation0.25059286
Coefficient of variation (CV)1.1327791
Kurtosis0.761788
Mean0.22121953
Median Absolute Deviation (MAD)0.1146
Skewness1.292721
Sum372.97613
Variance0.062796781
MonotonicityNot monotonic
2025-10-22T19:51:30.021939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1128
 
0.5%
0.27
 
0.4%
0.1197
 
0.4%
0.3147
 
0.4%
0.1997
 
0.4%
0.2236
 
0.4%
0.256
 
0.4%
0.1386
 
0.4%
0.2696
 
0.4%
0.1096
 
0.4%
Other values (981)1620
96.1%
ValueCountFrequency (%)
1.33 × 10-52
0.1%
1.83 × 10-51
0.1%
2.23 × 10-52
0.1%
2.55 × 10-51
0.1%
2.64 × 10-51
0.1%
3.43 × 10-51
0.1%
3.81 × 10-51
0.1%
5.59 × 10-51
0.1%
5.99 × 10-52
0.1%
6.79 × 10-51
0.1%
ValueCountFrequency (%)
0.9952
0.1%
0.9891
0.1%
0.9881
0.1%
0.9871
0.1%
0.9861
0.1%
0.9851
0.1%
0.9841
0.1%
0.9811
0.1%
0.9771
0.1%
0.9741
0.1%

id
Text

Distinct1437
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:30.332687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters37092
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1224 ?
Unique (%)72.6%

Sample

1st row2plbrEY59IikOBgBGLjaoe
2nd row6dOtVTDdiauQNBQEDOtlAB
3rd row7ne4VBA60CxGM75vw0EYad
4th row1d7Ptw3qYcfpdLNL5REhtJ
5th row5vNRhkKd0yEAg8suGBpjeY
ValueCountFrequency (%)
6ai3ezq4o3huop6dhudph34
 
0.2%
6watfblvb0x077xweovc2k4
 
0.2%
1es7auahqvapicoh3qmkdl4
 
0.2%
13vxuhw3o8yt7vwridqso44
 
0.2%
28drn6tqo95mrvo0jqeo5c4
 
0.2%
2nyeymeqydifsyytl2bwd64
 
0.2%
3tfed7ysjgnifxeleqwx3r3
 
0.2%
3mg9jync1nihbthlglzrwp3
 
0.2%
1cobocuwyi2ostofolmrs63
 
0.2%
4orozzuy6gown4ugqvazmf3
 
0.2%
Other values (1427)1650
97.9%
2025-10-22T19:51:30.806207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2850
 
2.3%
3839
 
2.3%
0825
 
2.2%
1792
 
2.1%
5783
 
2.1%
6766
 
2.1%
4751
 
2.0%
7747
 
2.0%
o635
 
1.7%
j617
 
1.7%
Other values (52)29487
79.5%
Distinct53
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:31.090028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.8505338
Min length3

Characters and Unicode

Total characters11550
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowmainstream
2nd rowmainstream
3rd rowmainstream
4th rowmainstream
5th rowmainstream
ValueCountFrequency (%)
modern356
20.3%
classic162
 
9.2%
throwback150
 
8.5%
reggaeton101
 
5.8%
soft98
 
5.6%
chill66
 
3.8%
pop57
 
3.2%
global50
 
2.8%
punk50
 
2.8%
mainstream47
 
2.7%
Other values (46)618
35.2%
2025-10-22T19:51:31.525974image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1093
 
9.5%
a1040
 
9.0%
o1033
 
8.9%
r905
 
7.8%
n851
 
7.4%
c755
 
6.5%
t697
 
6.0%
s680
 
5.9%
i624
 
5.4%
m578
 
5.0%
Other values (17)3294
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)11550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1093
 
9.5%
a1040
 
9.0%
o1033
 
8.9%
r905
 
7.8%
n851
 
7.4%
c755
 
6.5%
t697
 
6.0%
s680
 
5.9%
i624
 
5.4%
m578
 
5.0%
Other values (17)3294
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1093
 
9.5%
a1040
 
9.0%
o1033
 
8.9%
r905
 
7.8%
n851
 
7.4%
c755
 
6.5%
t697
 
6.0%
s680
 
5.9%
i624
 
5.4%
m578
 
5.0%
Other values (17)3294
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1093
 
9.5%
a1040
 
9.0%
o1033
 
8.9%
r905
 
7.8%
n851
 
7.4%
c755
 
6.5%
t697
 
6.0%
s680
 
5.9%
i624
 
5.4%
m578
 
5.0%
Other values (17)3294
28.5%

type
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
audio_features
1686 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters23604
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaudio_features
2nd rowaudio_features
3rd rowaudio_features
4th rowaudio_features
5th rowaudio_features

Common Values

ValueCountFrequency (%)
audio_features1686
100.0%

Length

2025-10-22T19:51:31.701153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-22T19:51:31.836585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
audio_features1686
100.0%

Most occurring characters

ValueCountFrequency (%)
a3372
14.3%
u3372
14.3%
e3372
14.3%
d1686
7.1%
i1686
7.1%
_1686
7.1%
o1686
7.1%
f1686
7.1%
t1686
7.1%
r1686
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)23604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3372
14.3%
u3372
14.3%
e3372
14.3%
d1686
7.1%
i1686
7.1%
_1686
7.1%
o1686
7.1%
f1686
7.1%
t1686
7.1%
r1686
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)23604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3372
14.3%
u3372
14.3%
e3372
14.3%
d1686
7.1%
i1686
7.1%
_1686
7.1%
o1686
7.1%
f1686
7.1%
t1686
7.1%
r1686
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)23604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3372
14.3%
u3372
14.3%
e3372
14.3%
d1686
7.1%
i1686
7.1%
_1686
7.1%
o1686
7.1%
f1686
7.1%
t1686
7.1%
r1686
7.1%
Distinct72
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
2025-10-22T19:51:32.152968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters37092
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.5%

Sample

1st row37i9dQZF1DXcBWIGoYBM5M
2nd row37i9dQZF1DXcBWIGoYBM5M
3rd row37i9dQZF1DXcBWIGoYBM5M
4th row37i9dQZF1DXcBWIGoYBM5M
5th row37i9dQZF1DXcBWIGoYBM5M
ValueCountFrequency (%)
37i9dqzf1dx7f6t2n2fegs150
 
8.9%
50rfc7x949czs4chz4fpde101
 
6.0%
37i9dqzf1dwtyibj6yeqeu100
 
5.9%
37i9dqzf1dwtwnem1iyyoj98
 
5.8%
37i9dqzf1dwxrqgorjj26u91
 
5.4%
37i9dqzf1dx3kdv0ichem960
 
3.6%
3gzggrvdn9ttuy8n7699pd50
 
3.0%
37i9dqzf1eqoqch7bwiyb750
 
3.0%
72gglgq63euhg3cycv42ju50
 
3.0%
37i9dqzevxbmdohdwvn2tf50
 
3.0%
Other values (62)886
52.6%
2025-10-22T19:51:32.827045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71734
 
4.7%
91707
 
4.6%
F1626
 
4.4%
Z1518
 
4.1%
31502
 
4.0%
Q1483
 
4.0%
11463
 
3.9%
D1414
 
3.8%
d1411
 
3.8%
i1346
 
3.6%
Other values (52)21888
59.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
71734
 
4.7%
91707
 
4.6%
F1626
 
4.4%
Z1518
 
4.1%
31502
 
4.0%
Q1483
 
4.0%
11463
 
3.9%
D1414
 
3.8%
d1411
 
3.8%
i1346
 
3.6%
Other values (52)21888
59.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
71734
 
4.7%
91707
 
4.6%
F1626
 
4.4%
Z1518
 
4.1%
31502
 
4.0%
Q1483
 
4.0%
11463
 
3.9%
D1414
 
3.8%
d1411
 
3.8%
i1346
 
3.6%
Other values (52)21888
59.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
71734
 
4.7%
91707
 
4.6%
F1626
 
4.4%
Z1518
 
4.1%
31502
 
4.0%
Q1483
 
4.0%
11463
 
3.9%
D1414
 
3.8%
d1411
 
3.8%
i1346
 
3.6%
Other values (52)21888
59.0%

Interactions

2025-10-22T19:51:12.962950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:55.495514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:57.234809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.055987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:00.691789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.185839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.659721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.307749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.760929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:08.315535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.001009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.455755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:13.084778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:55.647064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:57.386066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.179603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:00.814313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.304937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.782523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.429268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.887727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:08.444342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.116067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.578872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:13.224214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:55.801398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:57.528658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.318761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:00.945255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.434733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.915284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.561034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.022103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:08.585777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.241668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.715857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:13.481423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:55.935545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:57.737460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.434592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.065094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.552159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:04.038371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.677356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.152137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:08.714617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.361609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.840490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:13.596700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:56.065677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:57.926802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.552493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.185890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.678918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:04.170197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.791705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.274524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:08.837399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.480333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.959072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:13.713767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:56.192791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:58.061475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.676483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.307678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.790548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:04.297619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.906433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.408214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:09.088403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.599679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:12.082539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:13.834125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:56.448507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:58.211001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.797949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.435633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.907691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:04.430389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.023792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.537185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:09.216738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.720274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:12.209601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:13.951621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:56.573240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:58.362896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:59.915110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.554229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.020469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:04.549245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.138300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.665010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:09.350003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.835897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:12.333114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:14.080863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:56.719995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:58.526593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:00.070948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.697979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.164428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:04.677530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.268737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.792952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:09.488340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:10.961895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:12.462816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:14.204583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:56.849052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:58.662398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:00.193428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.822867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.291571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:04.925463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.399569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:07.929431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:09.613378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.083230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:12.595114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:14.325522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:56.976664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:58.787377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:00.317072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:01.940644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.414718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.051839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.515400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:08.056174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:09.742179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.219363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:12.715285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:14.461545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:57.104388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:50:58.922613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:00.443615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:02.065295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:03.537783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:05.182050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:06.639698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:08.185308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:09.878833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:11.338258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-22T19:51:12.835128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-10-22T19:51:32.972893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodeplaylist_genrespeechinesstempotime_signaturetrack_popularityvalence
acousticness1.0000.050-0.110-0.527-0.0380.018-0.032-0.3370.0600.266-0.032-0.1800.182-0.019-0.077
danceability0.0501.000-0.227-0.080-0.084-0.002-0.1200.0890.1500.2320.295-0.0890.215-0.0090.339
duration_ms-0.110-0.2271.0000.0230.067-0.038-0.034-0.0690.0750.192-0.165-0.0480.095-0.036-0.084
energy-0.527-0.0800.0231.0000.0040.0040.1390.6470.0740.3050.0780.1210.222-0.0020.291
instrumentalness-0.038-0.0840.0670.0041.0000.039-0.087-0.2550.0390.186-0.2100.0250.131-0.049-0.071
key0.018-0.002-0.0380.0040.0391.000-0.027-0.0060.2350.0700.0230.0120.067-0.018-0.004
liveness-0.032-0.120-0.0340.139-0.087-0.0271.0000.1270.0320.1120.057-0.0020.0410.017-0.042
loudness-0.3370.089-0.0690.647-0.255-0.0060.1271.0000.0560.2830.1430.0560.2620.0640.211
mode0.0600.1500.0750.0740.0390.2350.0320.0561.0000.2160.1100.0820.0000.0370.000
playlist_genre0.2660.2320.1920.3050.1860.0700.1120.2830.2161.0000.1730.1680.2300.1310.133
speechiness-0.0320.295-0.1650.078-0.2100.0230.0570.1430.1100.1731.0000.0570.117-0.1290.052
tempo-0.180-0.089-0.0480.1210.0250.012-0.0020.0560.0820.1680.0571.0000.1120.005-0.015
time_signature0.1820.2150.0950.2220.1310.0670.0410.2620.0000.2300.1170.1121.0000.1240.082
track_popularity-0.019-0.009-0.036-0.002-0.049-0.0180.0170.0640.0370.131-0.1290.0050.1241.000-0.008
valence-0.0770.339-0.0840.291-0.071-0.004-0.0420.2110.0000.1330.052-0.0150.082-0.0081.000

Missing values

2025-10-22T19:51:14.688946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-22T19:51:15.243032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

energytempodanceabilityplaylist_genreloudnesslivenessvalencetrack_artisttime_signaturespeechinesstrack_popularitytrack_hrefuritrack_album_nameplaylist_nameanalysis_urltrack_idtrack_nametrack_album_release_dateinstrumentalnesstrack_album_idmodekeyduration_msacousticnessidplaylist_subgenretypeplaylist_id
00.592157.9690.521pop-7.7770.12200.535Lady Gaga, Bruno Mars30.0304100https://api.spotify.com/v1/tracks/2plbrEY59IikOBgBGLjaoespotify:track:2plbrEY59IikOBgBGLjaoeDie With A SmileToday's Top Hitshttps://api.spotify.com/v1/audio-analysis/2plbrEY59IikOBgBGLjaoe2plbrEY59IikOBgBGLjaoeDie With A Smile2024-08-160.00000010FLjwfpbxLmW8c25Xyc2N062516680.30802plbrEY59IikOBgBGLjaoemainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
10.507104.9780.747pop-10.1710.11700.438Billie Eilish40.035897https://api.spotify.com/v1/tracks/6dOtVTDdiauQNBQEDOtlABspotify:track:6dOtVTDdiauQNBQEDOtlABHIT ME HARD AND SOFTToday's Top Hitshttps://api.spotify.com/v1/audio-analysis/6dOtVTDdiauQNBQEDOtlAB6dOtVTDdiauQNBQEDOtlABBIRDS OF A FEATHER2024-05-170.0608007aJuG4TFXa2hmE4z1yxc3n122103730.20006dOtVTDdiauQNBQEDOtlABmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
20.808108.5480.554pop-4.1690.15900.372Gracie Abrams40.036893https://api.spotify.com/v1/tracks/7ne4VBA60CxGM75vw0EYadspotify:track:7ne4VBA60CxGM75vw0EYadThe Secret of Us (Deluxe)Today's Top Hitshttps://api.spotify.com/v1/audio-analysis/7ne4VBA60CxGM75vw0EYad7ne4VBA60CxGM75vw0EYadThat’s So True2024-10-180.0000000hBRqPYPXhr1RkTDG3n4Mk111663000.21407ne4VBA60CxGM75vw0EYadmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
30.910112.9660.670pop-4.0700.30400.786Sabrina Carpenter40.063481https://api.spotify.com/v1/tracks/1d7Ptw3qYcfpdLNL5REhtJspotify:track:1d7Ptw3qYcfpdLNL5REhtJShort n' SweetToday's Top Hitshttps://api.spotify.com/v1/audio-analysis/1d7Ptw3qYcfpdLNL5REhtJ1d7Ptw3qYcfpdLNL5REhtJTaste2024-08-230.0000004B4Elma4nNDUyl6D5PvQkj001572800.09391d7Ptw3qYcfpdLNL5REhtJmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
40.783149.0270.777pop-4.4770.35500.939ROSÉ, Bruno Mars40.260098https://api.spotify.com/v1/tracks/5vNRhkKd0yEAg8suGBpjeYspotify:track:5vNRhkKd0yEAg8suGBpjeYAPT.Today's Top Hitshttps://api.spotify.com/v1/audio-analysis/5vNRhkKd0yEAg8suGBpjeY5vNRhkKd0yEAg8suGBpjeYAPT.2024-10-180.0000002IYQwwgxgOIn7t3iF6ufFD001699170.02835vNRhkKd0yEAg8suGBpjeYmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
50.582116.7120.700pop-5.9600.08810.785Chappell Roan40.035694https://api.spotify.com/v1/tracks/0WbMK4wrZ1wFSty9F7FCguspotify:track:0WbMK4wrZ1wFSty9F7FCguGood Luck, Babe!Today's Top Hitshttps://api.spotify.com/v1/audio-analysis/0WbMK4wrZ1wFSty9F7FCgu0WbMK4wrZ1wFSty9F7FCguGood Luck, Babe!2024-04-050.0000001WAjjRMfZjEXtB0lQrAw6Q0112184240.05020WbMK4wrZ1wFSty9F7FCgumainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
60.561150.0690.669pop-6.5380.09540.841Addison Rae40.041188https://api.spotify.com/v1/tracks/6MzofobZt2dm0Kf1hTThFzspotify:track:6MzofobZt2dm0Kf1hTThFzDiet PepsiToday's Top Hitshttps://api.spotify.com/v1/audio-analysis/6MzofobZt2dm0Kf1hTThFz6MzofobZt2dm0Kf1hTThFzDiet Pepsi2024-08-090.0096200XA403JTounqFh2owquBXu1101696980.49506MzofobZt2dm0Kf1hTThFzmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
70.247148.1010.467pop-12.0020.17000.126Billie Eilish40.043193https://api.spotify.com/v1/tracks/3QaPy1KgI7nu9FJEQUgn6hspotify:track:3QaPy1KgI7nu9FJEQUgn6hHIT ME HARD AND SOFTToday's Top Hitshttps://api.spotify.com/v1/audio-analysis/3QaPy1KgI7nu9FJEQUgn6h3QaPy1KgI7nu9FJEQUgn6hWILDFLOWER2024-05-170.0002717aJuG4TFXa2hmE4z1yxc3n062614670.61203QaPy1KgI7nu9FJEQUgn6hmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
80.41694.9260.492pop-10.4390.20300.297Gigi Perez40.025471https://api.spotify.com/v1/tracks/0UYnhUfnUj5adChuAXvLUBspotify:track:0UYnhUfnUj5adChuAXvLUBSailor SongToday's Top Hitshttps://api.spotify.com/v1/audio-analysis/0UYnhUfnUj5adChuAXvLUB0UYnhUfnUj5adChuAXvLUBSailor Song2024-07-260.0000864DWrYvfGXRE8ko5ZxlIpit1112119790.68600UYnhUfnUj5adChuAXvLUBmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
90.722119.9730.769pop-5.4850.11100.570The Weeknd, Playboi Carti40.050792https://api.spotify.com/v1/tracks/1Es7AUAhQvapIcoh3qMKDLspotify:track:1Es7AUAhQvapIcoh3qMKDLTimelessToday's Top Hitshttps://api.spotify.com/v1/audio-analysis/1Es7AUAhQvapIcoh3qMKDL1Es7AUAhQvapIcoh3qMKDLTimeless (with Playboi Carti)2024-09-270.0000032IRxVVqbSbqHJo8Zx50LYn0112560000.05841Es7AUAhQvapIcoh3qMKDLmainstreamaudio_features37i9dQZF1DXcBWIGoYBM5M
energytempodanceabilityplaylist_genreloudnesslivenessvalencetrack_artisttime_signaturespeechinesstrack_popularitytrack_hrefuritrack_album_nameplaylist_nameanalysis_urltrack_idtrack_nametrack_album_release_dateinstrumentalnesstrack_album_idmodekeyduration_msacousticnessidplaylist_subgenretypeplaylist_id
16760.50399.9700.792latin-8.0440.09590.381Ayra Starr40.062668https://api.spotify.com/v1/tracks/1rrqJ9QkOBYJlsZgqqwxgBspotify:track:1rrqJ9QkOBYJlsZgqqwxgBRushAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/1rrqJ9QkOBYJlsZgqqwxgB1rrqJ9QkOBYJlsZgqqwxgBRush2022-09-160.0005706CvEsGBD3JdbDKpmJaXn2E111850930.03691rrqJ9QkOBYJlsZgqqwxgBafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16770.501103.9930.637latin-6.1480.09900.431Rotimi, Wale40.187068https://api.spotify.com/v1/tracks/6XM53PbvlzhuNtJZtpl7RPspotify:track:6XM53PbvlzhuNtJZtpl7RPThe Beauty of BecomingAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/6XM53PbvlzhuNtJZtpl7RP6XM53PbvlzhuNtJZtpl7RPIn My Bed2019-12-130.0000591AUSfQC9x3SsqNQhFq05l7001854610.22906XM53PbvlzhuNtJZtpl7RPafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16780.485100.0530.843latin-8.7350.10700.689Drake, Tems40.195068https://api.spotify.com/v1/tracks/08XWh5c0BMyD1nKVxxl91zspotify:track:08XWh5c0BMyD1nKVxxl91zCertified Lover BoyAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/08XWh5c0BMyD1nKVxxl91z08XWh5c0BMyD1nKVxxl91zFountains (with Tems)2021-09-030.0675003SpBlxme9WbeQdI9kx7KAV0101924180.181008XWh5c0BMyD1nKVxxl91zafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16790.549123.8790.613latin-8.1870.11600.780Tems50.231071https://api.spotify.com/v1/tracks/2dFqK2ZkYB9Xc47gr3xXWlspotify:track:2dFqK2ZkYB9Xc47gr3xXWlIf Orange Was A PlaceAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/2dFqK2ZkYB9Xc47gr3xXWl2dFqK2ZkYB9Xc47gr3xXWlReplay2021-09-140.0000000x2ntwkM3GoLVAPjAOPrWv191800000.09702dFqK2ZkYB9Xc47gr3xXWlafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16800.649113.0090.788latin-9.6740.12000.229Davido, Musa Keys40.053269https://api.spotify.com/v1/tracks/2kaH2Z8ezDUKf6fNw250rZspotify:track:2kaH2Z8ezDUKf6fNw250rZTimelessAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/2kaH2Z8ezDUKf6fNw250rZ2kaH2Z8ezDUKf6fNw250rZUNAVAILABLE (feat. Musa Keys)2023-03-300.0136006lI21W76LD0S3vC55GrfSS121699120.25302kaH2Z8ezDUKf6fNw250rZafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16810.422124.3570.573latin-7.6210.10200.693Libianca50.067876https://api.spotify.com/v1/tracks/26b3oVLrRUaaybJulow9kzspotify:track:26b3oVLrRUaaybJulow9kzPeopleAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/26b3oVLrRUaaybJulow9kz26b3oVLrRUaaybJulow9kzPeople2022-12-060.0000135Hmh6N8oisrcuZKa8EY5dn0101847910.551026b3oVLrRUaaybJulow9kzafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16820.725105.0160.711latin-8.3150.11000.530Omah Lay40.094174https://api.spotify.com/v1/tracks/1wADwLSkYhrSmy4vdy6BRnspotify:track:1wADwLSkYhrSmy4vdy6BRnBoy AloneAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/1wADwLSkYhrSmy4vdy6BRn1wADwLSkYhrSmy4vdy6BRnsoso2022-07-140.1290005NLjxx8nRy9ooUmgpOvfem031830570.42401wADwLSkYhrSmy4vdy6BRnafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16830.80999.0050.724latin-5.0220.07650.606Davido, FAVE40.092969https://api.spotify.com/v1/tracks/7vKXc90NT5WBm3UTT4iTVGspotify:track:7vKXc90NT5WBm3UTT4iTVGTimelessAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/7vKXc90NT5WBm3UTT4iTVG7vKXc90NT5WBm3UTT4iTVGKANTE (feat. Fave)2023-03-300.0000006lI21W76LD0S3vC55GrfSS061940400.18207vKXc90NT5WBm3UTT4iTVGafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16840.64283.3890.463latin-4.4740.06860.339Future, Drake, Tems40.340082https://api.spotify.com/v1/tracks/59nOXPmaKlBfGMDeOVGrIKspotify:track:59nOXPmaKlBfGMDeOVGrIKI NEVER LIKED YOUAfro-Latin Partyhttps://api.spotify.com/v1/audio-analysis/59nOXPmaKlBfGMDeOVGrIK59nOXPmaKlBfGMDeOVGrIKWAIT FOR U (feat. Drake & Tems)2022-04-290.0000006tE9Dnp2zInFij4jKssysL111898930.314059nOXPmaKlBfGMDeOVGrIKafro-latinaudio_features0oU30cCr8klmMsuOKHDLkh
16850.890126.8810.645pop-4.9850.37600.421Alan Walker, Ina Wroldsen40.128069https://api.spotify.com/v1/tracks/2GE3k8I0Sbh0puCjI15KGyspotify:track:2GE3k8I0Sbh0puCjI15KGyBarcelonaScandi Pophttps://api.spotify.com/v1/audio-analysis/2GE3k8I0Sbh0puCjI15KGy2GE3k8I0Sbh0puCjI15KGyBarcelona2024-06-130.00000934yBJhr8zlBAHMEMSwrISN162050870.25902GE3k8I0Sbh0puCjI15KGyscandiaudio_features59z06GgF6TTDbm5cr1RZUC